Attack surfaces explained: Types, examples, and reduction

Modern cybersecurity teams scale their observation and remediation capabilities faster than ever before. The attack surfaces they manage, though, are growing even faster. This growth is a real consequence of the modern cloud environment: cloud stacks are complex, automated, and composed of intricate components that no single person fully understands or owns. Cloud services spin up, execute work, and disappear before you have a chance to test their security. Dependency chains are deep and change in large blocks. CI pipelines create and move code, images, and access credentials fast enough that it’s hard to track them. Things can go wrong at any point in the system, and the rate of churn can cause your attack surfaces to change very fast. The uncomfortable truth is that you can’t observe and remediate your way out of an expanding attack surface.

Observation and remediation are scaled up in straightforward ways; your security teams will add specialized scanners, increase logging, create more complex dashboards, and generate additional alerts. It’s an important first set of steps towards tracking and explaining the mess, but it adds a lot of work. All of the added visibility explodes your remediation queues and puts pressure on your engineers, often with inherited vulnerabilities they didn’t introduce. Your system-level risk reports show constant change, while the impact on your security metrics gradually shrinks to 0.

At some point, once your systems grow too large and too quickly, and remediation becomes too expensive, it’s time to understand what constitutes an attack surface. Then you can minimize it by (re)designing your systems to expose less in the first place and manage the growth. It’s harder, slower work, and it’s the only kind of work that holds.

What is an attack surface?

An attack surface is everything an attacker could possibly touch. If your environment were a house, this would be every door you meant to add, every window you forgot about, and a few crawlspaces no one remembers building.

Most people, when thinking of attack surfaces, immediately come up with open ports and public APIs as potential attack vectors. That’s only the visible edge. The surface extends far beyond them:

• Every library you pulled in, including transitive dependencies, no one reviewed.

• Container images with a full OS, never tracked, audited, patched, or tested.

• Cloud services shipped with permissive defaults, meant to be temporary and never revisited.

• Trivial CI jobs running with admin credentials, capable of rewriting all of production.

Each of these felt like a small risk when first introduced. Each was easy to justify when speed mattered more than restraint. And each was assumed to be easy to reverse later.

The problem is accumulation, and it’s exacerbated by extremely effective automation and AI. Attackers need just one accumulated path through to a vulnerable pod somewhere in your systems to deliver a lot of pain. And the bigger the surface, the more of the accumulated risks they can reach. Over time, the chances that they’ll find something misconfigured, outdated, scaled too fast, or simply forgotten increase. And, once an attacker gets in, each of these components becomes a stepping stone towards corrupting large swaths of your system.

This is why defining, reducing, and managing your attack surface start to matter much more than keeping track of vulnerability counts and scan results. If the shape keeps expanding, the math works against you, no matter how good your monitoring is.

Types of attack surfaces

Attack surfaces come in all kinds of shapes and sizes. They stack, overlap, and bleed into each other. It’s important to understand what they are and how they interact, since breaches often start at the edges or somewhere no one was watching.

Most teams deal with:

Digital attack surface

This is what most people immediately think about when they talk about attack surfaces. Anything exposed to the open internet belongs in this category. APIs, load balancers, identity endpoints, SaaS hooks. Some of these are intentional and actively used. Some of these are leftovers or are rarely used. Attackers gladly take advantage of either kind of surface component.

Physical attack surface

Not glamorous, so it’s often forgotten. It’s also where you’re likely most vulnerable. A stolen device, an insecure device connected to sensitive systems, or an unlocked rack can undo years of careful policy work in minutes. Someone holding the door open for a stranger or losing a copy of their office keys can open attack paths you rarely consider.

Human and social engineering surface

Phishing remains effective because it exploits incentives and established habits: urgency, authority, helpfulness, and routine. It just needs one to click on a link that makes sense in context—a doc review, a build failure alert, a password reset, a vendor invoice, a calendar invite that looks exactly like yesterday’s.

You can’t patch your way out of the social engineering attack surface. Automated tools, such as CI systems and service accounts, often hold privileges humans never would; the social engineering paths to access into a CI system are typically very short and not well secured.

Software supply chain surface

This is where volumes quietly spiral out of control: Dependencies you didn’t write and aren’t aware of. Images you didn’t build. Pipelines that pull code and binaries from everywhere. Even “hardened” ecosystems ship with triple-digit common vulnerability and exploit (CVE) counts: Red Hat UBI images average ~190 CVEs; Air Force Iron Bank images average ~110. None of those vulnerabilities feels urgent; systems work without a hitch for long periods of time. And then, suddenly, one CVE matters a lot.

Obscure surfaces are most interesting to attackers. They’re probably not going to focus their attention on the one you spent most of your time securing.

Why software attack surfaces are growing

Attack surface growth is a natural byproduct of shipping modern software. Left unchecked, it expands continuously as complexity, automation, and reuse pile on. It is a growth that won’t ever stop.

Dependency sprawl and open-source reliance

An engineer adds a single library to solve a specific problem, and it quietly pulls in dozens more. Most of those dependencies are never reviewed, because the time and expertise required don’t scale. They still ship anyway. This is how reuse works in practice and at scale.

Complex cloud-native and containerized environments

Cloud native systems are in constant motion. Containers are created and destroyed in a continuous dance, services shift around as architecture evolves, and configurations drift as environments scale. Security controls that seemed “locked down” last month might no longer be correct today. Static security assumptions don’t survive this volume of churn, and attack surfaces change in ways that are hard to track, reason about, or entirely control.

Expanding CI/CD pipelines and automation

CI systems now have the keys to the kingdom. They decide what gets built, what gets shipped, and what runs in production. Speed and convenience have pushed them into positions of implicit trust, with broad privileges that are rarely revisited. As they make it easier to scale systems in response to product and user demands, the attack surface scales along with them.

Compliance pressures and visibility gaps

Audits demand proof of what’s running, how it was built, what it depends on, who can access it, and whether known risks are controlled. Teams assemble that proof—SBOMs, inventories, scan reports, access reviews, access control lists, POA&Ms—often by hand and against a frozen snapshot of the system.

Then everything changes. New images are built, dependencies shift, permissions creep, services scale, and pipelines evolve. The compliance artifacts immediately fall behind reality. That lag creates a visibility gap: the system that can be proven to be compliant is no longer the system that’s actually running. And that gap is where risk hides.

Emerging workloads like AI

AI is a force multiplier for existing attack surfaces across the software supply chain, infrastructure, authentication control, identities, and CI/CD automation. It adds more code, more data, and more pipelines, while removing human review. AI systems retrain, rebuild, and redeploy models continuously, and those model changes directly trigger updates to artifacts, pipelines, and production behavior. Usually, these changes happen faster than security practices can adapt.

So your attack surface reduction and management can’t be a one-off project. The surface keeps moving, so your reduction and management efforts have to be a continuous practice and process.

How to identify and measure your attack surface

It’s easy to underestimate your true attack surface, especially on the software side. Your scanner output and architecture diagrams usually will only show you a subset of what is actually reachable once your production systems are running under typical load. You can only reduce and manage what you can measure, and you can’t measure things you don’t know exist. And the gap, the things you don’t know exist and can’t manage or measure, is where attackers are most likely to find a way in.

As a rule of thumb, if a component isn’t standardized, it’s difficult to reliably inventory it. And anything that’s outside of your inventory won’t show up in your management and reduction efforts. A disciplined approach to managing your attack surfaces begins at the start of your software deployment pipelines.

Map dependencies and components

Start by listing and counting everything that runs in your environments: applications, services, container images, libraries, and build systems. Keep track of intermediary systems and transitive dependencies too. Don’t stop at the recipes and instructions that your CI system sees; actually explore your systems once they’re live and inventory what’s inside each pod and cluster.

Use SBOMs and provenance for visibility

Software bills of materials (SBOMs) provide a concrete list of included components. Provenance adds context: where artifacts came from, how they were created, how they were integrated into your systems, and who vouched for them. They work together to make your opaque and invisible system components into something trackable, measurable, and reportable.

Continuous monitoring vs. point-in-time assessments

Your static scans become outdated the minute they’re executed; modern cloud infrastructure can spin up systems and resources at sub-second speeds. They’re not effective as security controls, so it’s better for you to treat them as inputs to your security practice.

Shift measurement from something that’s occasionally present to a persistent practice. Exposure is a function of impact, severity, and, crucially, age. Track how long vulnerabilities remain reachable, how fast your fixes propagate, and whether your remediation practice is reducing exposure over time. Continuous measurement reveals how long vulnerabilities persist and whether your fixes have been effective.

Assume your reports are snapshots of the past, and ensure your practice continuously updates visibility, tracking, remediation, and effectiveness checks.

Best practices to reduce your attack surface

Scanning for known problems is easy enough to do at scale. Keeping known problems from being accessible by cyberattackers, though, requires painstaking work up front. This is especially true if you want your system to be robust against emergent and new vulnerabilities and threats. Attack surface reduction is about designing your systems to expose less while staying nimble and moving fast.

Start from minimal, secure-by-default foundations

Vulnerabilities and attack surfaces that appear early in your CI process have ample opportunity to grow and sneak their way into production. If you start with minimal images, you ship less base software that you’ll then have to defend. If the images are secure and patched by default, anything that depends on an insecure component will be filtered out automatically. Not only is the system more elegant, but the math also lines up. Your teams will avoid monitoring, reporting on, and then racing to remediate messes if they never inherited them in the first place.

Eliminate unnecessary components and bloat

If you can’t start with secure-by-default images, you can instead debloat and damage control further into your pipelines. When you remove unnecessary components and debloat your system, you’re performing damage control. In tests, DIY hardening cuts CVEs by ~64%, which is nothing to scoff at. Of course, you can get the best of both worlds: start with images that have 0 known CVEs, and once they’re ready for production, debloat and remove anything that helped your CI and isn’t strictly necessary to deploy to production.

Continuously patch and rebuild software artifacts

Attackers don’t time their attacks to your quarterly business calendars, so why does your patch cycle? Modern exploits move on a resolution of days and hours, and slow quarterly scan and patch cycles guarantee your exposure windows are too long. To collapse your windows and match your attack surface to exploitation patterns, make sure your software artifacts are continuously patched and rebuilt. Your security and engineering backlogs will thank you.

Secure build pipelines and CI/CD systems

Your CI/CD pipelines control and decide on everything that makes it into production. That’s an enormous amount of power that’s rarely scrutinized. Lock them down. Apply the same validation, tracking, and security processes to the pipelines as you apply to the software artifacts they’re producing. If attackers hijack your build, every downstream scan and control is security theater.

Automate compliance with SBOMs and attestations

Nothing manual scales fast enough to match the growth of modern cloud systems, and that includes compliance. You’ll want to use the same kinds of automation for SBOM and attestation generation as you use for everything else in your stack. You’ll know you’re doing well when your engineering teams willingly use your automated systems, instead of routing around laborious and time-consuming manual processes.

Align security and development teams without disrupting workflows

When security impacts speed in visible ways, the speedier efforts tend to trump the secure ones. Surface reduction that seamlessly integrates with CI/CD makes your security work blend into the background, allowing developers to use it without noticing or fighting it. And your risks and attack vector exposures will go down.

At a regular schedule, perhaps quarterly, you should review and audit your processes along these dimensions. Ensure that the entire organization uses minimal images, continuously rebuilds, and propagates fixes to production as soon as they’re available. Double-check that your CI/CD pipelines are held to the same performance and security standards as your production systems.

Eliminate software vulnerabilities at the source with Chainguard

Most engineering teams’ attack surfaces encompass people, processes, infrastructure, and software. You’ll likely employ different tools and practices for each layer, including physical access protection, audits, IAM access for network controls, and various forms of monitoring.

Your largest and fastest-growing attack surfaces, though, are probably software-based and sit squarely within your software supply chain. And most of its expansion happens upstream, before most controls ever get to see it or have a say in its management. Chainguard is purpose-built to reduce supply-chain contributions to your attack surface at the source:

• Works alongside your existing security stack (endpoint, IAM, network, monitoring) by reducing software-related exposure before it reaches production.

• Provides minimal, secure-by-default containers, libraries, and VMs, so teams start with fewer vulnerabilities before patching or monitoring even begins.

• Continuously rebuilds and patches artifacts, eliminating vulnerabilities at the source and shrinking the backlog that burdens dev and security teams.

• Delivers built-in SBOMs, provenance, and compliance features, improving visibility and audit readiness without slowing developers down.

• Seamlessly blends into your CI/CD pipelines, so your developers consume and produce trusted artifacts without changing their workflows and introducing friction.

For organizations operating at scale—where CI/CD automation, regulatory demands, and supply-chain risk collide—Chainguard reduces inherited exposure upstream. That’s why companies like Snowflake and GitLab rely on it to complement their broader security strategies.

If your attack surface keeps growing through software reuse, talk to an expert about reducing the software supply chain attack surface with Chainguard.